Eric De Grasse
Chief Technology Officer
InfoTech Europe
(a division of The Project Counsel Group)
5 August 2019 (Paris, France) – Over the weekend I began to pour over some of the papers to be presented next year at ICLAAIBD 2020, the International Conference on Legal Analytics, Artificial Intelligence and Big Data. It brings together some of the leading academic scientists, researchers and research scholars in those areas. Since it will be in our “home town” we are taking an active role.
One aspect continues to intrigue us: for lawyers (especially litigators) performing accurate legal research has always been the core skill of successful lawyering. But over the last two years better and better tools has appeared in litigators’ key tool box: legal analytics. As I noted in my client memo last month, Owen Byrd … entrepreneur, lawyer, data geek, politico, urbanist, developer, and Chief Evangelist and Legal Counsel at Lex Machina … broke it out like this:
Legal analytics involves mining data contained in case documents and docket entries, and then aggregating that data to provide previously unknowable insights into the behavior of the individuals (judges and lawyers), organizations (parties, courts, law firms), and the subjects of lawsuits (such as patents) that populate the litigation ecosystem. Litigators use legal analytics to reveal trends and patterns in past litigation that inform legal strategy and anticipate outcomes in current cases.
Data-driven insights from legal analytics do not replace legal research or reasoning, or lawyers themselves. They are a supplement, both prior to and during litigation.
Yep. Legal analytics is just Moneyball for lawyers.
Legal analytics relies on advanced technologies, such as machine learning and natural language processing, to clean up, structure, and analyze raw data from millions of case dockets and documents. And in-house counsel also use legal analytics to inform key business decisions, such as who to hire for outside counsel. What is the track record of firm Z in litigating certain kinds of cases? Which firms have the most experience in this area, and what were the outcomes of those cases?
It is also the reason, I suspect, that we have seen so many in-house corporate law departments eschew the standard “usual suspects” e-discovery vendors … FTI, Nuix, Relativity, etc., etc. … building out some pretty fancy analytics tools using Python and R programming language. We are engaged in a long-term project in Zurich using Python and the results are staggering. It puts the “usual suspects in the shade.
As I have noted before, attorneys are demanding a combination of descriptive, predictive and prescriptive analytics to data and few vendors can provide that, hence the turn to Python and R-based options.
And more importantly, at the Consero corporate law department workshop held just before summer break, the mantra was: “data and analytics can be a powerful and sustainable source of competitive advantage”
And let’s face it: keeping their data (and the analytic process) in house makes a lot of sense. Many did note that for all the current enthusiasm (and media-driven “hype”), the surface of this field has only been scratched up to now.
It has been only recently that the tried and tired e-discovery vendors have “seen the light”, vendor after vendor announcing the formation of a data products group to make a big push into data science.
We have certainly seen in it other areas, such as the partnership of UK-based insurance law firm BLM with the London School of Economics, to develop litigation prediction models as part of a wider move into legal analytics that is now spreading across the legal market. That focus is on volume litigation, which is often more amenable to AI and machine learning, and also high value complex claims. As I noted in a post last year, that effort includes exploring AI and statistical predictive models for valuing disputes and predicting outcomes, predicting cost overruns and case length and managing litigation at a portfolio level.
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